1. Field of the Invention
The present invention relates to an ultrasonic color flow mapping system and a method for filtering an ultrasonic Doppler signal.
2. Description of the Related Art
With the development of electronics, an ultrasonic diagnostic system which is widely used in a general medical treatment has been remarkably improved in its performance and thus plays a role of an auxiliary stethoscope. In principle, the ultrasonic diagnostic method uses an acousto-physical response or acousto-physical information of a living organism texture such as reflection, scattering and absorption occurring when an ultrasonic wave passes through the living organism texture.
A Doppler method has been proposed as a method for realization of an ultrasonic scanned signal as an image. A diagnostic apparatus adopting the Doppler method displays a frequency shift amount together with a signal scattering intensity as images, to thereby estimate the circulation of blood in the living body. However, such a conventional diagnostic apparatus cannot but obtain only blood stream information of a constant depth according to the beam direction.
Thus, the Doppler video system capable of displaying a color image demodulates a received signal and then digitizes the demodulation signal for processing, which can provide an excellent effect in which a blood stream flowing in the heart or a main blood vessel can be described as a two-dimensional (2D) image. The color Doppler video system can display both tomogram and blood stream information. To identify the tomogram and the blood stream information with each other, the color Doppler video system displays the tomogram as monochrome and the blood stream information as color. In this case, the information of the blood stream flowing toward the direction of the scanned ultrasonic beam is displayed as the warm color and that of toward the counter-direction thereof is displayed as the cool color. Accordingly, the information of the blood stream can be displayed more accurately.
Meanwhile, the Doppler signal having demodulated by the above color Doppler video system contains a signal from a feeble blood stream having a relatively high frequency and a relatively small magnitude, and a signal from a soft texture having a relatively low frequency and a relatively large magnitude. In general, the former is called a Doppler signal and the latter is called a clutter signal. Here, a low-frequency signal, that is, a clutter signal impedes an accurate detection of the blood stream information. Thus, to accurately detect the blood stream information, the clutter signal should be properly removed. The clutter signal contains a very large direct-current (DC) component in general, and has several hundred times an amplitude as large as that of the Doppler signal, which has a bad influence upon an accurate image regeneration, as will be described later. For this reason, the color Doppler video system uses a moving target indicator (MTI) filter being a kind of a high-pass filter, to remove such a clutter signal.
The MTI filter is a filter applied from the principle of an MTI radar, which is divided into an infinite impulse response (IIR) filter and a finite impulse response (FIR) filter. The FIR filter adopts a relatively simple hardware structure and possesses a high stability with respect to a coefficient variation, with no transient phenomena, when compared with the IIR filter. However, since the FIR filter has a very sluggish frequency characteristic curve, it is not so easy to determine a cut-off frequency necessary for removing the clutter signal. In comparison, the IIR filter of the low degree can obtain a desired steep frequency characteristic curve relatively easily, which causes it to be widely used in real implementation.
FIG. 1 is a block diagram showing a conventional ultrasonic color flow mapping system. As can be seen in the drawing, the conventional ultrasonic color flow mapping system includes a plurality of two-dimensional (2D) IIR filters 11 through 14, an auto-correlation estimator 15 and a look-up table 16. The video system uses N sequential complex variables I(n) and Q(n) in which n=0, 1, . . . , N-1 of a practical data set as inputs, in order to calculate an average velocity and power of the blood stream. Here, assuming that the inputs of the 2'nd order IIR filters 11 through 14 are denoted as x(n) and the outputs thereof are denoted as y(n), an input and output relationship is represented by the following equation (1). EQU y(n)=.alpha..sub.0 x(n)+.alpha..sub.1 x(n-1)+.alpha..sub.2 x(n-2)-.beta..sub.1 y(n-1)-.beta..sub.2 y(n-2) (1)
Here, .alpha..sub.0, .alpha..sub.1, .alpha..sub.2, .beta..sub.1 and .beta..sub.2 are filter coefficients which are variables properly set according to the characteristic of the filter. Here, in the case when it is defined that n=0, 1, . . . , N-1, a transient response with respect to the clutter signal which is removed by filtering it according as how x(-1), x(-2), y(-1) and y(-2) are defined, can be varied.
Meanwhile, the auto-correlation output of the auto-correlation estimator 15 according to the inputs I2(n) and Q2(n) in FIG. 2 is defined as the following equation (2). ##EQU1##
Here, the auto-correlation R(0) from the auto-correlation estimator 15 is contributed for calculating the power of the Doppler signal and the auto-correlations Re(R(1)) and Im(R(1)) are contributed for calculating the average frequency of the Doppler signal through the look-up table 16.
However, the color Doppler video system for the abdomen generally uses N of eight through 16, in order to calculate the average velocity and the power of the blood stream with respect to the pixel. Accordingly, although filtering is performed using the IIR filter, a transient response with respect to a feeble clutter signal still remains. Also, since the clutter signal is greater than the Doppler signal by 40 through 50 dB, even a transient response with respect to the clutter signal can have a bad influence upon calculation of the blood stream velocity.
Thus, various studies are under progress in order to reduce the effect of a transient response with respect to the clutter signal. Recently, a method for calculating a blood stream velocity after excluding several initial output data which can have a comparatively big influence upon the transient response among the filtered outputs, has been proposed. According to this method, in the FIG. 1 technology, the indices of the outputs I1(n) and Q1(n) with respect to the inputs I(n) and Q(n) of the 2'nd order IIR filters are n in which n=0, 1, . . . , N-1. However, the indices of the outputs I1(n) and Q1(n) with respect to the same inputs are n in which n=n1, n1+1, . . . , N-1, and the indices of the outputs I2(n) and Q2(n) having passed through the downstream 2'nd order IIR filters are n in which n=n2, n2+1, . . . , N-1. Here, n2 is equal to or greater than n1. In conclusion, as the number of the initial output data, that is, n1 and n2 are increased, the effect of the transient response with respect to the clutter signal can be reduced.
However, although the above method can effectively reduce the effect of the transient response with respect to the clutter signal, the number of data input to the auto-correlation estimator and used for the blood stream velocity calculation is reduced, which causes a reliable blood stream velocity and the relevant information not to be obtained.